Projeto
grupo 6sigma
2023-05-10
Bibliotecas
library(skimr)
library(txtplot)
library(cluster)
library(tidyverse)
library(caret)
library(factoextra)
library(mclust)
library(readr)
library(naniar)
library(here)
library(psych)
library(corrplot)
library(haven)
library(dplyr)
library(magrittr)
options(repr.plot.width=6, repr.plot.height=6)Leitura dos dados
dados <- readRDS(here("data", "STU_QQQ_5.rds"))
dadosOmissos de cada coluna
colSums(is.na(dados)) PV1SCIE PV1READ PV1MATH SOCONPA BODYIMA GCAWAREP INTCULTP ATTIMMP JOYREADP
5377 41320 5377 534956 536507 612004 612004 612004 520815
PRESUPP PASCHPOL PQSCHOOL EMOSUPP CURSUPP FLFAMILY FLSCHOOL FLCONICT FLCONFIN
519825 520641 519999 520772 519740 612004 612004 612004 612004
INFOJOB2 INFOJOB1 INFOCAR ICTOUTSIDE ICTCLASS SOIAICT AUTICT COMPICT INTICT
390359 390359 382412 283481 275263 310331 306205 299011 292761
USESCH HOMESCH ENTUSE BEINGBULLIED BELONG DISCRIM GLOBMIND AWACOM RESPECT
298901 288643 271459 147660 77867 612004 612004 612004 612004
COGFLEX PERSPECT INTCULT ATTIMM GCAWARE GCSELFEFF MASTGOAL RESILIENCE SWBP
612004 612004 612004 612004 612004 612004 71452 74444 124918
EUDMO GFOFAIL WORKMAST COMPETE ATTLNACT PERCOOP PERCOMP PISADIFF SCREADDIFF
105309 69899 66094 55755 46324 147207 132669 59460 73950
SCREADCOMP JOYREAD TEACHINT ADAPTIVITY STIMREAD EMOSUPS PERFEED DIRINS TEACHSUP
70810 38233 60699 44478 43247 129289 64409 57856 60365
DISCLIMA ICTRES WEALTH HEDRES CULTPOSS HOMEPOS ICTSCH ICTHOME ESCS
32662 15676 13737 17705 23446 11400 256547 249475 14379
STUBMI CHANGE SCCHANGE FCFMLRTY TMINS SMINS LMINS MMINS AGE
540769 381656 379993 612004 222268 148178 143593 143986 0
GRADE ST061Q01NA ST060Q01NA ST059Q03TA ST059Q02TA ST059Q01TA ST016Q01NA IMMIG HISEI
3019 118181 144404 136286 131373 130630 118802 32568 47647
BFMJ2 BMMJ1 HISCED FISCED MISCED ISCEDL ST001D01T REPEAT PROGN
110980 84576 17657 32575 21433 6869 0 36532 0
OCOD3 OCOD2 OCOD1 ST004D01T CNT
0 0 0 2 0 # A coluna dos Paises não tem nulos
sum(is.na(dados$CNT))[1] 0Países Existentes na base de dados
unique(dados$CNT)<labelled<character>[80]>: Country code 3-character
[1] ALB ARE ARG AUS AUT BEL BGR BIH BLR BRA BRN CAN CHE CHL COL CRI CZE DEU DNK DOM ESP EST FIN FRA GBR GEO GRC HKG HRV HUN
[31] IDN IRL ISL ISR ITA JOR JPN KAZ KOR KSV LBN LTU LUX LVA MAC MAR MDA MEX MKD MLT MNE MYS NLD NOR NZL PAN PER PHL POL PRT
[61] QAT QAZ QCI QMR QRT ROU RUS SAU SGP SRB SVK SVN SWE TAP THA TUR UKR URY USA VNM
Labels:Visualização das características de cada um dos países
dados %>%
mutate(NumNulos = rowSums(is.na(.))) %>%
group_by(CNT) %>%
summarize(TotalNulos = sum(NumNulos),
NumLinhas = n(),
PercentNulos = (TotalNulos / (NumLinhas * ncol(dados))) * 100,
MeanScience = mean(PV1SCIE, na.rm = TRUE),
MeanRead = mean(PV1READ, na.rm = TRUE),
MeanMath = mean(PV1MATH, na.rm = TRUE),
.groups = "drop") %>%
arrange(desc(NumLinhas))mutate: new variable 'NumNulos' (double) with 75 unique values and 0% NA
group_by: one grouping variable (CNT)
summarize: now 80 rows and 7 columns, ungroupedIrlanda
Irlanda <- dados %>% filter(CNT == "IRL")filter: removed 606,427 rows (99%), 5,577 rows remainingIrlandaCaracterísticas da Irlanda
dim(Irlanda)
describe(Irlanda)# str(Irlanda)# summary(Irlanda)skimr::skim(Irlanda)── Data Summary ────────────────────────
Values
Name Irlanda
Number of rows 5577
Number of columns 104
_______________________
Column type frequency:
character 5
numeric 99
________________________
Group variables None summary(Irlanda) PV1SCIE PV1READ PV1MATH SOCONPA BODYIMA GCAWAREP INTCULTP
Min. :174.8 Min. :192.9 Min. :191.9 Min. :-2.9377 Min. :-2.4111 Min. : NA Min. : NA
1st Qu.:435.2 1st Qu.:456.4 1st Qu.:446.6 1st Qu.:-0.3986 1st Qu.:-0.8487 1st Qu.: NA 1st Qu.: NA
Median :495.9 Median :520.0 Median :501.3 Median : 0.3691 Median :-0.2393 Median : NA Median : NA
Mean :495.0 Mean :518.0 Mean :499.3 Mean : 0.1605 Mean :-0.2001 Mean :NaN Mean :NaN
3rd Qu.:558.0 3rd Qu.:582.0 3rd Qu.:554.0 3rd Qu.: 0.9078 3rd Qu.: 0.1330 3rd Qu.: NA 3rd Qu.: NA
Max. :781.4 Max. :799.9 Max. :739.7 Max. : 0.9078 Max. : 1.8425 Max. : NA Max. : NA
NA's :281 NA's :620 NA's :5577 NA's :5577
ATTIMMP JOYREADP PRESUPP PASCHPOL PQSCHOOL EMOSUPP CURSUPP
Min. : NA Min. :-3.6715 Min. :-3.4786 Min. :-3.1082 Min. :-3.5025 Min. :-3.5088 Min. :-3.3762
1st Qu.: NA 1st Qu.:-0.6539 1st Qu.:-0.2014 1st Qu.:-0.5031 1st Qu.:-0.6598 1st Qu.:-0.0472 1st Qu.:-0.5694
Median : NA Median : 0.3075 Median : 0.3382 Median : 0.1937 Median :-0.2402 Median : 0.7390 Median :-0.0296
Mean :NaN Mean : 0.2375 Mean : 0.5037 Mean : 0.1606 Mean : 0.1294 Mean : 0.1837 Mean : 0.0315
3rd Qu.: NA 3rd Qu.: 1.2659 3rd Qu.: 1.0193 3rd Qu.: 0.7313 3rd Qu.: 0.7808 3rd Qu.: 0.7390 3rd Qu.: 0.5505
Max. : NA Max. : 2.2623 Max. : 2.3781 Max. : 2.4534 Max. : 2.0484 Max. : 0.7390 Max. : 4.7142
NA's :5577 NA's :668 NA's :639 NA's :649 NA's :630 NA's :663 NA's :627
FLFAMILY FLSCHOOL FLCONICT FLCONFIN INFOJOB2 INFOJOB1 INFOCAR
Min. : NA Min. : NA Min. : NA Min. : NA Min. :-1.4803 Min. :-0.9812 Min. :-1.9166
1st Qu.: NA 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA 1st Qu.:-1.4728 1st Qu.:-0.9778 1st Qu.:-0.7602
Median : NA Median : NA Median : NA Median : NA Median :-0.2439 Median :-0.2917 Median :-0.3090
Mean :NaN Mean :NaN Mean :NaN Mean :NaN Mean :-0.3424 Mean :-0.1055 Mean :-0.3243
3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: 0.2691 3rd Qu.: 0.5109 3rd Qu.: 0.2327
Max. : NA Max. : NA Max. : NA Max. : NA Max. : 1.4601 Max. : 1.9988 Max. : 2.2501
NA's :5577 NA's :5577 NA's :5577 NA's :5577 NA's :198 NA's :198 NA's :166
ICTOUTSIDE ICTCLASS SOIAICT AUTICT COMPICT INTICT
Min. :-1.3048 Min. :-1.2188 Min. :-2.1763 Min. :-2.5144 Min. :-2.6033 Min. :-2.9505
1st Qu.:-1.2255 1st Qu.:-1.2188 1st Qu.:-0.6261 1st Qu.:-0.5023 1st Qu.:-0.3734 1st Qu.:-0.2057
Median :-0.3430 Median :-0.5142 Median :-0.1361 Median : 0.0979 Median : 0.0537 Median : 0.0321
Mean :-0.2891 Mean :-0.3713 Mean :-0.0344 Mean : 0.0719 Mean : 0.1829 Mean : 0.2804
3rd Qu.: 0.2858 3rd Qu.: 0.2245 3rd Qu.: 0.5149 3rd Qu.: 0.4719 3rd Qu.: 0.7148 3rd Qu.: 0.7470
Max. : 2.4969 Max. : 2.4394 Max. : 2.3635 Max. : 2.0258 Max. : 1.9885 Max. : 2.6672
NA's :292 NA's :167 NA's :1111 NA's :989 NA's :875 NA's :711
USESCH HOMESCH ENTUSE BEINGBULLIED BELONG DISCRIM GLOBMIND
Min. :-1.7161 Min. :-2.3008 Min. :-3.59400 Min. :-0.7823 Min. :-3.2367 Min. : NA Min. : NA
1st Qu.:-1.0028 1st Qu.:-0.8794 1st Qu.:-0.42123 1st Qu.:-0.7823 1st Qu.:-0.6464 1st Qu.: NA 1st Qu.: NA
Median :-0.3566 Median :-0.4215 Median :-0.08675 Median : 0.1462 Median :-0.3184 Median : NA Median : NA
Mean :-0.3929 Mean :-0.4364 Mean :-0.01139 Mean : 0.1370 Mean :-0.1504 Mean :NaN Mean :NaN
3rd Qu.: 0.1482 3rd Qu.:-0.0054 3rd Qu.: 0.31383 3rd Qu.: 0.8233 3rd Qu.: 0.2243 3rd Qu.: NA 3rd Qu.: NA
Max. : 3.3041 Max. : 3.3070 Max. : 4.24440 Max. : 3.8591 Max. : 2.7562 Max. : NA Max. : NA
NA's :578 NA's :414 NA's :237 NA's :1100 NA's :213 NA's :5577 NA's :5577
AWACOM RESPECT COGFLEX PERSPECT INTCULT ATTIMM GCAWARE GCSELFEFF
Min. : NA Min. : NA Min. : NA Min. : NA Min. : NA Min. : NA Min. : NA Min. : NA
1st Qu.: NA 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA
Median : NA Median : NA Median : NA Median : NA Median : NA Median : NA Median : NA Median : NA
Mean :NaN Mean :NaN Mean :NaN Mean :NaN Mean :NaN Mean :NaN Mean :NaN Mean :NaN
3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA
Max. : NA Max. : NA Max. : NA Max. : NA Max. : NA Max. : NA Max. : NA Max. : NA
NA's :5577 NA's :5577 NA's :5577 NA's :5577 NA's :5577 NA's :5577 NA's :5577 NA's :5577
MASTGOAL RESILIENCE SWBP EUDMO GFOFAIL WORKMAST
Min. :-2.5252 Min. :-3.16750 Min. :-3.06660 Min. :-2.1464 Min. :-1.8939 Min. :-2.73650
1st Qu.:-0.7346 1st Qu.:-0.55360 1st Qu.:-0.59280 1st Qu.:-0.9827 1st Qu.:-0.4342 1st Qu.:-0.72330
Median :-0.0940 Median :-0.06140 Median :-0.10640 Median :-0.2670 Median : 0.1097 Median :-0.10150
Mean :-0.1171 Mean :-0.04521 Mean :-0.09236 Mean :-0.1725 Mean : 0.2049 Mean :-0.08416
3rd Qu.: 0.5761 3rd Qu.: 0.38580 3rd Qu.: 0.65020 3rd Qu.: 0.2621 3rd Qu.: 0.8794 3rd Qu.: 0.56930
Max. : 1.8524 Max. : 2.36930 Max. : 1.23860 Max. : 1.7411 Max. : 1.8905 Max. : 1.81640
NA's :211 NA's :186 NA's :238 NA's :222 NA's :169 NA's :215
COMPETE ATTLNACT PERCOOP PERCOMP PISADIFF SCREADDIFF
Min. :-2.3450 Min. :-2.5375 Min. :-2.1428 Min. :-1.9892 Min. :-1.27200 Min. :-1.8876
1st Qu.:-0.4112 1st Qu.:-0.6583 1st Qu.:-0.9391 1st Qu.:-0.6142 1st Qu.:-1.27200 1st Qu.:-0.5240
Median : 0.1956 Median : 0.4626 Median :-0.2119 Median : 0.2020 Median : 0.27050 Median :-0.0697
Mean : 0.1623 Mean : 0.1116 Mean :-0.1707 Mean : 0.2024 Mean :-0.01798 Mean : 0.0060
3rd Qu.: 0.7871 3rd Qu.: 1.0844 3rd Qu.: 0.6012 3rd Qu.: 0.6912 3rd Qu.: 0.52060 3rd Qu.: 0.5082
Max. : 2.0054 Max. : 1.0844 Max. : 1.6762 Max. : 2.0378 Max. : 3.00640 Max. : 2.7752
NA's :134 NA's :101 NA's :1210 NA's :886 NA's :95 NA's :127
SCREADCOMP JOYREAD TEACHINT ADAPTIVITY STIMREAD EMOSUPS
Min. :-2.4403 Min. :-2.7316 Min. :-2.2177 Min. :-2.26520 Min. :-2.30030 Min. :-2.4468
1st Qu.:-0.5196 1st Qu.:-0.7553 1st Qu.:-0.5905 1st Qu.:-0.57860 1st Qu.:-0.62000 1st Qu.:-0.6576
Median : 0.1222 Median :-0.1241 Median : 0.1744 Median : 0.01560 Median : 0.02360 Median : 0.5029
Mean : 0.1224 Mean :-0.0752 Mean : 0.1353 Mean :-0.01276 Mean : 0.06284 Mean : 0.1758
3rd Qu.: 0.8214 3rd Qu.: 0.5572 3rd Qu.: 0.7227 3rd Qu.: 0.45990 3rd Qu.: 0.51350 3rd Qu.: 1.0346
Max. : 1.8839 Max. : 2.6574 Max. : 1.8245 Max. : 2.00730 Max. : 2.08710 Max. : 1.0346
NA's :123 NA's :58 NA's :57 NA's :98 NA's :69 NA's :813
PERFEED DIRINS TEACHSUP DISCLIMA ICTRES WEALTH
Min. :-1.6391 Min. :-2.9425 Min. :-2.7106 Min. :-2.71240 Min. :-2.91160 Min. :-3.7420
1st Qu.:-0.3253 1st Qu.:-0.7874 1st Qu.:-0.5284 1st Qu.:-0.65380 1st Qu.:-0.45560 1st Qu.:-0.3392
Median : 0.3993 Median :-0.2540 Median : 0.2048 Median :-0.04240 Median :-0.00130 Median : 0.1140
Mean : 0.3081 Mean :-0.2003 Mean : 0.1558 Mean : 0.04238 Mean : 0.09396 Mean : 0.1841
3rd Qu.: 0.7743 3rd Qu.: 0.3737 3rd Qu.: 1.3140 3rd Qu.: 0.87100 3rd Qu.: 0.49490 3rd Qu.: 0.6363
Max. : 2.0165 Max. : 1.8202 Max. : 1.3411 Max. : 2.03450 Max. : 3.60100 Max. : 4.1586
NA's :80 NA's :47 NA's :45 NA's :42 NA's :36 NA's :29
HEDRES CULTPOSS HOMEPOS ICTSCH ICTHOME ESCS STUBMI
Min. :-4.4911 Min. :-1.9156 Min. :-2.9556 Min. : 0.000 Min. : 0.000 Min. :-4.5030 Min. : 7.18
1st Qu.:-0.8449 1st Qu.:-0.4948 1st Qu.:-0.4106 1st Qu.: 5.000 1st Qu.: 8.000 1st Qu.:-0.5003 1st Qu.: 19.20
Median :-0.1357 Median : 0.4235 Median : 0.1519 Median : 6.000 Median : 9.000 Median : 0.1707 Median : 21.60
Mean :-0.2061 Mean : 0.3584 Mean : 0.1650 Mean : 6.144 Mean : 8.746 Mean : 0.1261 Mean : 23.92
3rd Qu.: 0.1806 3rd Qu.: 1.2337 3rd Qu.: 0.6926 3rd Qu.: 8.000 3rd Qu.:10.000 3rd Qu.: 0.8024 3rd Qu.: 24.91
Max. : 1.2099 Max. : 2.3457 Max. : 5.0791 Max. :10.000 Max. :11.000 Max. : 2.9612 Max. :148.76
NA's :59 NA's :136 NA's :29 NA's :84 NA's :56 NA's :58 NA's :2743
CHANGE SCCHANGE FCFMLRTY TMINS SMINS LMINS MMINS
Min. :0.0000 Min. :0.0000 Min. : NA Min. : 180 Min. : 0 Min. : 0.0 Min. : 0.0
1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.: NA 1st Qu.:1640 1st Qu.: 120 1st Qu.: 160.0 1st Qu.: 160.0
Median :0.0000 Median :0.0000 Median : NA Median :1680 Median : 160 Median : 180.0 Median : 200.0
Mean :0.5299 Mean :0.3059 Mean :NaN Mean :1728 Mean : 144 Mean : 185.2 Mean : 192.9
3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.: NA 3rd Qu.:1800 3rd Qu.: 160 3rd Qu.: 200.0 3rd Qu.: 200.0
Max. :6.0000 Max. :2.0000 Max. : NA Max. :3000 Max. :2160 Max. :1800.0 Max. :1170.0
NA's :106 NA's :98 NA's :5577 NA's :1509 NA's :1112 NA's :1052 NA's :1045
AGE GRADE ST061Q01NA ST060Q01NA ST059Q03TA ST059Q02TA ST059Q01TA
Min. :15.25 Min. :-2.0000 Min. : 10.00 Min. :10.00 Min. : 0.000 Min. : 0.000 Min. : 0.000
1st Qu.:15.50 1st Qu.: 0.0000 1st Qu.: 40.00 1st Qu.:40.00 1st Qu.: 3.000 1st Qu.: 4.000 1st Qu.: 4.000
Median :15.67 Median : 0.0000 Median : 40.00 Median :42.00 Median : 4.000 Median : 5.000 Median : 4.000
Mean :15.70 Mean : 0.4052 Mean : 43.59 Mean :43.93 Mean : 3.363 Mean : 4.501 Mean : 4.311
3rd Qu.:15.92 3rd Qu.: 1.0000 3rd Qu.: 40.00 3rd Qu.:45.00 3rd Qu.: 4.000 3rd Qu.: 5.000 3rd Qu.: 5.000
Max. :16.25 Max. : 2.0000 Max. :120.00 Max. :80.00 Max. :40.000 Max. :26.000 Max. :40.000
NA's :925 NA's :1062 NA's :1025 NA's :950 NA's :951
ST016Q01NA IMMIG HISEI BFMJ2 BMMJ1 HISCED FISCED
Min. : 0.000 Min. :1.000 Min. :11.56 Min. :11.56 Min. :11.56 Min. :0.000 Min. :0.000
1st Qu.: 5.000 1st Qu.:1.000 1st Qu.:29.18 1st Qu.:25.94 1st Qu.:24.98 1st Qu.:4.000 1st Qu.:4.000
Median : 7.000 Median :1.000 Median :56.00 Median :33.76 Median :43.85 Median :5.000 Median :4.000
Mean : 6.725 Mean :1.275 Mean :52.76 Mean :44.04 Mean :45.50 Mean :5.011 Mean :4.453
3rd Qu.: 9.000 3rd Qu.:1.000 3rd Qu.:73.38 3rd Qu.:65.12 3rd Qu.:68.70 3rd Qu.:6.000 3rd Qu.:6.000
Max. :10.000 Max. :3.000 Max. :88.96 Max. :88.70 Max. :88.96 Max. :6.000 Max. :6.000
NA's :119 NA's :165 NA's :308 NA's :914 NA's :771 NA's :94 NA's :252
MISCED ISCEDL ST001D01T REPEAT PROGN OCOD3 OCOD2
Min. :0.000 Min. :2.000 Min. : 7.000 Min. :0.00000 Length:5577 Length:5577 Length:5577
1st Qu.:4.000 1st Qu.:2.000 1st Qu.: 9.000 1st Qu.:0.00000 Class :character Class :character Class :character
Median :5.000 Median :2.000 Median : 9.000 Median :0.00000 Mode :character Mode :character Mode :character
Mean :4.689 Mean :2.346 Mean : 9.405 Mean :0.06241
3rd Qu.:6.000 3rd Qu.:3.000 3rd Qu.:10.000 3rd Qu.:0.00000
Max. :6.000 Max. :3.000 Max. :11.000 Max. :1.00000
NA's :117 NA's :49
OCOD1 ST004D01T CNT
Length:5577 Min. :1.000 Length:5577
Class :character 1st Qu.:1.000 Class :character
Mode :character Median :2.000 Mode :character
Mean :1.502
3rd Qu.:2.000
Max. :2.000
Limpeza dos valores omissos
IrlandaSemiLimpa <- Irlanda %>% select(where(~ !all(is.na(.))))select: dropped 18 variables (GCAWAREP, INTCULTP, ATTIMMP, FLFAMILY, FLSCHOOL, …)dim(IrlandaSemiLimpa)[1] 5577 86nulos_percent <- IrlandaSemiLimpa %>%
summarise_all(~ mean(is.na(.)) * 100)summarise_all: now one row and 86 columns, ungroupedcolunas_com_nulos <- colnames(nulos_percent)[nulos_percent > 30]
colunas_com_nulos[1] "STUBMI"Irlanda <- select(Irlanda, -one_of(colunas_com_nulos))select: dropped one variable (STUBMI)options(scipen = 999)
Irlanda %>%
summarise(across(where(is.labelled), \(x) attributes(x)$label)) %>%
tidylog::pivot_longer(everything(), names_to = "Column", values_to = "Description") %>%
rowwise() %>%
mutate(Type = sapply(Column, \(x) class(dados[[x]])[3])) %>%
mutate(First_5_Values = sapply(Column, \(x) paste(head(na.omit(dados[[x]]), 5) , collapse = ", "))) %>%
mutate(naPercent = sapply(Column, \(x) Irlanda %>% zap_labels() %>% .[[x]] %>% is.na() %>% mean %>% "*"(100) %>% round(2))) -> dfI.descsummarise: now one row and 100 columns, ungrouped
pivot_longer: reorganized (SOCONPA, BODYIMA, GCAWAREP, INTCULTP, ATTIMMP, …) into (Column, Description) [was 1x100, now 100x2]
mutate: new variable 'Type' (character) with 2 unique values and 0% NA
mutate: new variable 'First_5_Values' (character) with 81 unique values and 0% NA
mutate: new variable 'naPercent' (double) with 70 unique values and 0% NAdfI.desc %>% View()
dfI.desclibrary(mice)
Irlanda <- IrlandaSemiLimpa %>% zap_labels() %>% mice(method = "norm.predict", m = 1)
iter imp variable
1 1 SOCONPA BODYIMA JOYREADP PRESUPP PASCHPOL PQSCHOOL EMOSUPP CURSUPP INFOJOB2 INFOJOB1 INFOCAR ICTOUTSIDE ICTCLASS SOIAICT AUTICT COMPICT INTICT USESCH HOMESCH ENTUSE BEINGBULLIED BELONG MASTGOAL RESILIENCE SWBP EUDMO GFOFAIL WORKMAST COMPETE ATTLNACT PERCOOP PERCOMP PISADIFF SCREADDIFF SCREADCOMP JOYREAD TEACHINT ADAPTIVITY STIMREAD EMOSUPS PERFEED DIRINS TEACHSUP DISCLIMA ICTRES WEALTH HEDRES CULTPOSS HOMEPOS ICTSCH ICTHOME ESCS STUBMI CHANGE SCCHANGE TMINS SMINS LMINS MMINS ST061Q01NA ST060Q01NA ST059Q03TA ST059Q02TA ST059Q01TA ST016Q01NA IMMIG HISEI BFMJ2 BMMJ1 HISCED FISCED MISCED REPEAT
2 1 SOCONPA BODYIMA JOYREADP PRESUPP PASCHPOL PQSCHOOL EMOSUPP CURSUPP INFOJOB2 INFOJOB1 INFOCAR ICTOUTSIDE ICTCLASS SOIAICT AUTICT COMPICT INTICT USESCH HOMESCH ENTUSE BEINGBULLIED BELONG MASTGOAL RESILIENCE SWBP EUDMO GFOFAIL WORKMAST COMPETE ATTLNACT PERCOOP PERCOMP PISADIFF SCREADDIFF SCREADCOMP JOYREAD TEACHINT ADAPTIVITY STIMREAD EMOSUPS PERFEED DIRINS TEACHSUP DISCLIMA ICTRES WEALTH HEDRES CULTPOSS HOMEPOS ICTSCH ICTHOME ESCS STUBMI CHANGE SCCHANGE TMINS SMINS LMINS MMINS ST061Q01NA ST060Q01NA ST059Q03TA ST059Q02TA ST059Q01TA ST016Q01NA IMMIG HISEI BFMJ2 BMMJ1 HISCED FISCED MISCED REPEAT
3 1 SOCONPA BODYIMA JOYREADP PRESUPP PASCHPOL PQSCHOOL EMOSUPP CURSUPP INFOJOB2 INFOJOB1 INFOCAR ICTOUTSIDE ICTCLASS SOIAICT AUTICT COMPICT INTICT USESCH HOMESCH ENTUSE BEINGBULLIED BELONG MASTGOAL RESILIENCE SWBP EUDMO GFOFAIL WORKMAST COMPETE ATTLNACT PERCOOP PERCOMP PISADIFF SCREADDIFF SCREADCOMP JOYREAD TEACHINT ADAPTIVITY STIMREAD EMOSUPS PERFEED DIRINS TEACHSUP DISCLIMA ICTRES WEALTH HEDRES CULTPOSS HOMEPOS ICTSCH ICTHOME ESCS STUBMI CHANGE SCCHANGE TMINS SMINS LMINS MMINS ST061Q01NA ST060Q01NA ST059Q03TA ST059Q02TA ST059Q01TA ST016Q01NA IMMIG HISEI BFMJ2 BMMJ1 HISCED FISCED MISCED REPEAT
4 1 SOCONPA BODYIMA JOYREADP PRESUPP PASCHPOL PQSCHOOL EMOSUPP CURSUPP INFOJOB2 INFOJOB1 INFOCAR ICTOUTSIDE ICTCLASS SOIAICT AUTICT COMPICT INTICT USESCH HOMESCH ENTUSE BEINGBULLIED BELONG MASTGOAL RESILIENCE SWBP EUDMO GFOFAIL WORKMAST COMPETE ATTLNACT PERCOOP PERCOMP PISADIFF SCREADDIFF SCREADCOMP JOYREAD TEACHINT ADAPTIVITY STIMREAD EMOSUPS PERFEED DIRINS TEACHSUP DISCLIMA ICTRES WEALTH HEDRES CULTPOSS HOMEPOS ICTSCH ICTHOME ESCS STUBMI CHANGE SCCHANGE TMINS SMINS LMINS MMINS ST061Q01NA ST060Q01NA ST059Q03TA ST059Q02TA ST059Q01TA ST016Q01NA IMMIG HISEI BFMJ2 BMMJ1 HISCED FISCED MISCED REPEAT
5 1 SOCONPA BODYIMA JOYREADP PRESUPP PASCHPOL PQSCHOOL EMOSUPP CURSUPP INFOJOB2 INFOJOB1 INFOCAR ICTOUTSIDE ICTCLASS SOIAICT AUTICT COMPICT INTICT USESCH HOMESCH ENTUSE BEINGBULLIED BELONG MASTGOAL RESILIENCE SWBP EUDMO GFOFAIL WORKMAST COMPETE ATTLNACT PERCOOP PERCOMP PISADIFF SCREADDIFF SCREADCOMP JOYREAD TEACHINT ADAPTIVITY STIMREAD EMOSUPS PERFEED DIRINS TEACHSUP DISCLIMA ICTRES WEALTH HEDRES CULTPOSS HOMEPOS ICTSCH ICTHOME ESCS STUBMI CHANGE SCCHANGE TMINS SMINS LMINS MMINS ST061Q01NA ST060Q01NA ST059Q03TA ST059Q02TA ST059Q01TA ST016Q01NA IMMIG HISEI BFMJ2 BMMJ1 HISCED FISCED MISCED REPEATWarning: Number of logged events: 6Irlanda <- complete(Irlanda)
Irlanda %>% summarise_all(~ mean(is.na(.)) * 100)summarise_all: now one row and 86 columns, ungroupeddescribe(Irlanda)colunas_nao_numericas <- names(Irlanda)[!sapply(Irlanda, is.numeric)]
colunas_nao_numericas[1] "PROGN" "OCOD3" "OCOD2" "OCOD1" "CNT" valores_unicos <- lapply(Irlanda[colunas_nao_numericas], unique)
# Exibir os valores únicos de cada coluna
for (i in seq_along(valores_unicos)) {
coluna <- colunas_nao_numericas[i]
valores <- valores_unicos[[i]]
cat("Valores únicos da coluna", coluna, ":\n")
print(valores)
cat("\n")
}Valores únicos da coluna PROGN :
[1] "03720001" "03720002" "03720005" "03720004" "03720003"
Valores únicos da coluna OCOD3 :
[1] "2160" "3432" "2651" "0000" "2140" "9705" "9704" "3153" "9999" "2654" "2500" "2341" "2130" "2412" "2652" "2264" "2131"
[18] "2330" "2411" "2250" "2611" "2642" "3111" "5312" "5164" "2240" "5142" "2161" "2513" "3355" "1431" "5141" "3332" "7231"
[35] "2300" "3115" "5311" "2266" "2510" "2221" "3421" "7411" "8341" "2641" "5412" "2111" "3422" "7100" "2166" "2643" "2144"
[52] "2635" "2210" "5223" "2212" "2632" "2512" "5411" "3258" "2152" "2634" "2222" "3435" "2000" "2269" "7115" "5110" "3240"
[69] "3118" "2265" "1412" "7512" "2262" "9998" "3423" "2100" "1411" "9411" "2113" "2200" "3434" "7210" "2659" "2261" "3334"
[86] "2514" "2150" "2655" "1000" "2120" "2145" "1120" "1211" "2400" "2342" "3119" "6121" "3210" "1346" "2151" "1221" "2114"
[103] "3213" "2230" "1345" "6100" "2142" "2636" "7110" "3311" "2410" "3211" "7511" "4412" "2354" "2163" "5111" "2211" "3431"
[120] "2431" "3257" "7543" "2523" "7111" "2146" "3350" "2633" "7126" "1324" "2600" "3251" "1113" "2653" "5131" "3411" "1341"
[137] "1210" "7122" "8332" "7541" "2355" "3522" "2612" "3140" "1420" "2430" "5221" "2621" "2656" "1349" "2520" "8320" "2353"
[154] "3510" "0110" "6000" "9703" "3359" "3412" "3500" "5120" "7212" "1311" "3513" "8331" "4413" "3410" "2112" "5300" "5322"
[171] "6111" "2310" "1310" "3521" "1323" "7522" "3230" "3511" "5230" "1100" "9123" "3430" "1330" "5132" "5163" "3339" "1114"
[188] "7422" "2631" "7124" "8340" "7410" "7232" "7230" "5241" "3123" "5413" "2149" "6222" "3413" "2421" "3154" "2610" "4110"
[205] "9701" "2162" "5419" "7123" "3322" "7120" "2650" "1212" "2141" "2165" "1200" "5240" "3110" "4226" "7233" "3255" "3321"
[222] "1320" "5410" "3100" "5320" "2164" "3323" "2519" "4416" "3344" "2267" "1111" "1222" "8342" "3152" "2153" "2511" "8141"
[239] "9214" "6113" "3212" "1112" "5220" "8211" "3114" "5212" "9210" "3314" "5153" "2320" "3222" "9320" "4120" "7000" "2132"
[256] "3420"
Valores únicos da coluna OCOD2 :
[1] "1120" "3153" "2133" "2140" "2611" "3311" "2142" "6121" "8322" "5223" "2166" "3214" "9705" "2411" "2211" "3431" "1324"
[18] "9999" "1346" "2114" "7110" "1323" "5414" "1420" "2510" "7212" "5153" "9704" "7100" "9703" "9122" "7510" "8332" "7122"
[35] "7111" "6110" "7410" "5411" "9412" "7231" "7411" "2212" "5164" "8300" "9311" "5320" "2651" "7511" "2511" "2652" "2262"
[52] "7131" "8131" "3421" "2320" "3334" "1210" "5221" "9210" "0000" "1221" "7115" "3513" "9100" "2161" "7123" "2420" "1412"
[69] "1330" "2330" "7322" "2512" "9313" "7540" "7413" "2300" "3434" "2310" "2221" "8100" "5152" "1000" "8331" "5413" "5165"
[86] "9622" "1430" "7126" "1320" "7127" "5141" "7422" "2632" "2412" "1219" "2654" "2131" "2160" "3113" "2145" "2264" "3313"
[103] "1410" "2269" "1211" "2520" "2421" "5412" "1400" "3350" "2144" "9702" "3154" "1300" "1342" "7230" "3257" "3118" "3422"
[120] "5220" "9330" "6210" "9111" "7543" "6111" "2152" "3114" "3423" "8211" "7113" "6100" "2113" "7124" "9998" "3310" "5111"
[137] "4211" "2151" "9320" "5120" "1321" "3511" "5243" "3152" "8311" "2100" "1212" "2643" "3300" "5200" "1411" "6113" "9312"
[154] "2410" "4110" "9110" "2433" "8212" "3324" "2400" "1200" "2523" "2642" "6120" "7112" "2413" "8342" "8000" "5132" "9333"
[171] "2250" "4416" "3339" "3112" "5321" "4412" "2132" "0310" "6222" "3123" "3332" "3432" "7121" "9216" "2423" "3333" "9112"
[188] "2641" "2359" "2261" "8344" "8340" "3240" "7119" "5230" "8219" "2210" "1345" "7213" "7412" "7133" "2165" "7120" "3512"
[205] "7125" "1220" "7315" "3254" "8160" "1439" "9701" "3320" "2141" "7500" "3150" "2149" "7400" "3100" "4212" "2630" "4321"
[222] "8320" "7233" "1213" "2143" "3412" "3321" "5131" "7421" "3521" "5112" "3351" "3500" "3122" "0210" "5242" "4226" "8183"
[239] "8122" "9611" "7114" "8341" "7210" "9300" "7316" "7522" "0110" "8142" "9629" "5222" "3315" "3211" "2164" "2636" "7314"
[256] "9621" "2635" "3411" "9129" "3352" "5113" "8159" "6130" "8312" "5312" "7520" "2263" "3258" "2655" "7200" "7549" "3110"
[273] "3323" "9214" "8121" "1341" "8330" "2341" "6129" "2240" "2500" "2513" "1431" "2622" "7222" "2634" "1322" "7215" "9411"
[290] "7310" "7515" "7512" "3255" "2354" "7300" "2162" "8130" "8157" "8189" "8350" "5322" "5419" "9212" "4120" "6221" "3132"
[307] "3520" "3221" "1223" "2153" "8154" "2659" "3155" "1312" "6000" "8120" "7531" "7221" "7523" "5130" "5163" "2522" "9620"
[324] "2656" "8172" "9510" "8140" "1110" "3133" "3000" "3312" "1100" "7000" "3213" "8210" "9334" "7214" "1340" "2260" "5410"
[341] "3134" "1349" "5110" "3355" "9600" "9123" "2265" "2431" "3510" "3121" "9610" "2529" "8343" "3200" "3250" "3115" "3119"
[358] "2519" "1114" "9000" "3514" "3435" "8114" "7232" "3331" "2111" "2163" "8111" "3120" "4222" "5329" "5240" "1311" "3340"
[375] "5244" "1310" "3522" "3116" "7534" "8141" "2150" "9329" "6114" "1112" "3353" "3143" "5142" "5246" "4214" "7521" "3130"
[392] "2619" "2110" "2434" "4000" "7317" "2200" "8310" "2422" "2130" "2000" "4312" "9215" "7130"
Valores únicos da coluna OCOD1 :
[1] "9999" "3343" "2330" "3412" "3334" "3311" "2221" "2423" "9412" "1324" "2166" "4226" "2265" "2341" "9111" "2634" "9701"
[18] "2342" "2651" "3321" "1114" "2411" "7512" "1221" "9705" "2641" "7318" "2310" "2359" "9704" "9411" "5141" "5322" "5311"
[35] "9110" "5223" "1322" "5320" "5120" "1212" "3350" "8160" "5230" "3312" "5312" "9320" "5142" "2269" "9703" "5131" "1439"
[52] "1420" "9112" "3340" "7531" "5321" "1211" "3513" "1412" "1219" "3256" "9100" "4310" "5200" "8200" "4120" "2212" "1341"
[69] "3512" "3251" "3342" "5246" "4110" "3221" "1411" "7410" "2300" "5132" "5222" "3434" "2632" "3211" "2161" "3332" "1120"
[86] "2200" "5164" "2654" "1330" "2514" "2652" "2643" "2421" "2611" "3431" "3344" "2131" "4211" "2163" "2355" "2113" "4221"
[103] "2164" "3257" "3352" "2267" "2211" "7543" "9702" "2262" "8131" "2222" "4412" "3341" "3432" "9998" "2500" "3359" "3253"
[120] "2160" "4131" "1321" "3314" "1210" "5220" "3355" "5221" "3313" "2621" "2000" "2352" "7400" "2512" "2622" "3339" "2642"
[137] "1349" "8183" "1000" "2264" "5161" "3353" "3423" "2635" "8157" "7313" "4225" "9329" "3354" "3212" "6121" "3230" "2145"
[154] "4100" "5410" "2230" "2353" "1346" "2210" "2612" "2266" "2655" "7533" "3213" "4222" "4224" "5151" "2511" "2142" "2510"
[171] "2151" "3323" "1344" "7231" "2519" "6113" "4410" "3300" "5412" "5413" "2260" "2320" "5165" "8322" "5244" "3333" "7511"
[188] "3411" "4312" "3521" "1200" "3214" "3240" "8000" "5111" "2140" "3255" "3500" "5300" "1223" "1300" "3111" "7323" "1410"
[205] "2412" "7200" "1340" "1342" "5163" "3320" "4313" "4223" "8153" "2354" "1430" "2513" "2133" "7131" "7513" "5240" "5419"
[222] "4200" "4220" "2261" "3200" "3252" "0000" "5329" "4311" "3422" "6100" "3119" "8212" "1400" "2162" "5153" "7515" "6111"
[239] "4400" "3514" "2410" "1345" "4210" "7534" "7320" "1431" "7549" "4320" "8211" "2100" "3322" "3154" "8320" "7221" "8141"
[256] "2620" "8100" "4300" "8142" "8189" "1320" "2433" "7125" "2420" "5150" "2351" "3310" "3258" "4212" "3155" "4000" "8300"
[273] "8332" "2521" "5140" "7230" "2422" "4321" "1343" "2350" "2400" "2656" "2263" "3222" "2149" "1220" "9321" "5110" "7314"
[290] "2431" "9333" "7322" "2250" "2520" "8159" "1222" "8130" "9621" "2659" "1213" "3510" "2630" "1112" "3315" "2144" "3123"
[307] "7300" "2152" "4229" "5414" "2132"
Valores únicos da coluna CNT :
[1] "IRL"colunas_nao_numericas <- colunas_nao_numericas[-5]
colunas_nao_numericas[1] "PROGN" "OCOD3" "OCOD2" "OCOD1"Irlanda[colunas_nao_numericas] <- lapply(Irlanda[colunas_nao_numericas], as.factor)
Irlanda <- Irlanda %>% select_if(is.numeric)select_if: dropped 5 variables (PROGN, OCOD3, OCOD2, OCOD1, CNT)Tratamento dos dados
Após realizada a limpeza dos dados, podemos então passar finalmente para a criação de PCA, a fim de se conseguir reduzir a dimensionalidade da base de dados, que possui 86 colunas. Antes de prosseguir, é necessário reter alguns pontos sobre como aplicar o PCA:
O PCA é mais eficaz quando as variáveis estão correlacionadas entre si.
Antes de aplicar o PCA, é importante verificar se as variáveis estão em escalas comparáveis. Variáveis em diferentes escalas podem ter diferentes variações e isso pode distorcer a análise do PCA.
Correlações das colunas
#Correlation plot with colors (too many attributes)
correlation <- cor(Irlanda)
corrplot(correlation)
#Correlation matrix
round(correlation, 3)KMO measure
KMO(correlation)Error in solve.default(r) :
system is computationally singular: reciprocal condition number = 2.30294e-34
matrix is not invertible, image not foundKaiser-Meyer-Olkin factor adequacy
Call: KMO(r = correlation)
Overall MSA = 0.5
MSA for each item =
PV1SCIE PV1READ PV1MATH SOCONPA BODYIMA JOYREADP PRESUPP PASCHPOL PQSCHOOL
0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5
EMOSUPP CURSUPP INFOJOB2 INFOJOB1 INFOCAR ICTOUTSIDE ICTCLASS SOIAICT AUTICT
0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5
COMPICT INTICT USESCH HOMESCH ENTUSE BEINGBULLIED BELONG MASTGOAL RESILIENCE
0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5
SWBP EUDMO GFOFAIL WORKMAST COMPETE ATTLNACT PERCOOP PERCOMP PISADIFF
0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5
SCREADDIFF SCREADCOMP JOYREAD TEACHINT ADAPTIVITY STIMREAD EMOSUPS PERFEED DIRINS
0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5
TEACHSUP DISCLIMA ICTRES WEALTH HEDRES CULTPOSS HOMEPOS ICTSCH ICTHOME
0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5
ESCS STUBMI CHANGE SCCHANGE TMINS SMINS LMINS MMINS AGE
0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5
GRADE ST061Q01NA ST060Q01NA ST059Q03TA ST059Q02TA ST059Q01TA ST016Q01NA IMMIG HISEI
0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5
BFMJ2 BMMJ1 HISCED FISCED MISCED ISCEDL ST001D01T REPEAT ST004D01T
0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 O KMO (Kaiser-Meyer-Olkin) é uma medida estatística utilizada para avaliar a adequação dos dados para realizar uma análise de fator ou análise de componentes principais (PCA). O valor do KMO varia de 0 a 1 e, quanto mais próximo de 1, melhor a adequação dos dados para uma análise de fator ou PCA. Valores de KMO acima de 0,6 são geralmente considerados adequados para análises exploratórias, enquanto valores acima de 0,8 são considerados muito bons.
Possiveis variáveis fracas
Vamos então visualizar as variáveis que não são adequadas para aplica o PCA
# kmo_result <- KMO(correlation)
# str(kmo_result)
# Obtém as variáveis com KMO abaixo de 0.6
# low_kmo_variables <- kmo_result$MSAi[kmo_result$MSAi < 0.6]
# length(low_kmo_variables)
# low_kmo_variablesPossiveis variáveis fortes
# Obtém as variáveis com KMO acima, ou igual, a 0.8
# high_kmo_variables <- kmo_result$MSAi[kmo_result$MSAi >= 0.8]
# length(high_kmo_variables)
# high_kmo_variablesTeste de esfericidade de Bartlett em uma matriz de correlação.
O teste de esfericidade de Bartlett é um teste estatístico que avalia se as variáveis em um conjunto de dados estão correlacionadas entre si. Ele verifica a hipótese nula de que a matriz de correlação populacional é uma matriz de identidade, o que significa que as variáveis são independentes e não correlacionadas.
cortest.bartlett(correlation)Warning: n not specified, 100 usedWarning: NaNs produced$chisq
[1] NaN
$p.value
[1] NaN
$df
[1] 3240Se o valor-p (p-value) calculado no teste de esfericidade de Bartlett for menor que 0.05, isso sugere evidências estatísticas para rejeitar a hipótese nula de que as variáveis são independentes e não correlacionadas, por outras palavras, existe uma correlação significativa entre as variáveis em questão. Isso significa que as variáveis não podem ser consideradas independentes e que existe algum grau de relação entre elas.
Estandardizar os dados
data_scaled <- scale(Irlanda)
data_scaled %>% glimpse() num [1:5577, 1:81] -1.03 0.812 0.596 1.815 1.875 ...
- attr(*, "dimnames")=List of 2
..$ : NULL
..$ : chr [1:81] "PV1SCIE" "PV1READ" "PV1MATH" "SOCONPA" ...
- attr(*, "scaled:center")= Named num [1:81] 495.025 518.043 499.328 0.156 -0.189 ...
..- attr(*, "names")= chr [1:81] "PV1SCIE" "PV1READ" "PV1MATH" "SOCONPA" ...
- attr(*, "scaled:scale")= Named num [1:81] 88.763 90.453 77.822 0.915 0.889 ...
..- attr(*, "names")= chr [1:81] "PV1SCIE" "PV1READ" "PV1MATH" "SOCONPA" ...PCA
Nesta parte, passaremos então para o processo de criação de PCA’s. Podemos então começar por pensar algumas ideias por alto, podendo ser estas:
- PCA de Desempenho Acadêmico:
- Variáveis de interesse: Pontuações em diferentes domínios acadêmicos, como leitura, matemática e ciências.
- Objetivo: Compreender a estrutura subjacente do desempenho acadêmico dos alunos e identificar padrões gerais de habilidades e competências.
- PCA de Fatores Socioeconômicos:
- Variáveis de interesse: Variáveis socioeconômicas, como o nível de educação dos pais, ocupação dos pais, renda familiar, acesso a recursos educacionais, etc.
- Objetivo: Explorar as relações entre os fatores socioeconômicos e o desempenho acadêmico dos alunos, bem como identificar grupos de alunos com perfis socioeconômicos semelhantes.
- PCA de Atitudes e Comportamentos:
- Variáveis de interesse: Variáveis relacionadas a atitudes, motivação, autoeficácia, disciplina, participação em atividades extracurriculares, etc.
- Objetivo: Identificar padrões de atitudes e comportamentos dos alunos que podem influenciar seu desempenho académico e bem-estar geral.
- PCA de Ambiente Escolar:
- Variáveis de interesse: Variáveis relacionadas ao ambiente escolar, como tamanho da turma, recursos educacionais disponíveis, clima escolar, relacionamento professor-aluno, etc.
- Objetivo: Explorar a relação entre o ambiente escolar e o desempenho dos alunos, bem como identificar características importantes do ambiente que afetam o aprendizado.
Colunas
colunas <- colnames(data_scaled)
colunas [1] "PV1SCIE" "PV1READ" "PV1MATH" "SOCONPA" "BODYIMA" "JOYREADP" "PRESUPP" "PASCHPOL"
[9] "PQSCHOOL" "EMOSUPP" "CURSUPP" "INFOJOB2" "INFOJOB1" "INFOCAR" "ICTOUTSIDE" "ICTCLASS"
[17] "SOIAICT" "AUTICT" "COMPICT" "INTICT" "USESCH" "HOMESCH" "ENTUSE" "BEINGBULLIED"
[25] "BELONG" "MASTGOAL" "RESILIENCE" "SWBP" "EUDMO" "GFOFAIL" "WORKMAST" "COMPETE"
[33] "ATTLNACT" "PERCOOP" "PERCOMP" "PISADIFF" "SCREADDIFF" "SCREADCOMP" "JOYREAD" "TEACHINT"
[41] "ADAPTIVITY" "STIMREAD" "EMOSUPS" "PERFEED" "DIRINS" "TEACHSUP" "DISCLIMA" "ICTRES"
[49] "WEALTH" "HEDRES" "CULTPOSS" "HOMEPOS" "ICTSCH" "ICTHOME" "ESCS" "STUBMI"
[57] "CHANGE" "SCCHANGE" "TMINS" "SMINS" "LMINS" "MMINS" "AGE" "GRADE"
[65] "ST061Q01NA" "ST060Q01NA" "ST059Q03TA" "ST059Q02TA" "ST059Q01TA" "ST016Q01NA" "IMMIG" "HISEI"
[73] "BFMJ2" "BMMJ1" "HISCED" "FISCED" "MISCED" "ISCEDL" "ST001D01T" "REPEAT"
[81] "ST004D01T" Visualizar correlações interessantes
correlation1 <- cor(data_scaled[, -1], data_scaled[, 1])
# corrplot(correlation1)
# Definir o limite de correlação desejado
limite_correlacao <- 0.3
# Identificar as colunas com correlação forte
colunas_fortes <- row.names(correlation1)[which(abs(correlation1) > limite_correlacao)]
colunas_fortes <- append(colunas_fortes, "PV1SCIE")
# Exibir as colunas com correlação forte
print(colunas_fortes)[1] "PV1READ" "PV1MATH" "PISADIFF" "SCREADDIFF" "SCREADCOMP" "JOYREAD" "ESCS" "PV1SCIE" Criação do PCA
pc1 <- principal(data_scaled, nfactors=ncol(data_scaled), rotate="none")Pearson correlations of the raw data were found
Warning: Matrix was not positive definite, smoothing was doneWarning: Matrix was not positive definite, smoothing was doneWarning: The matrix is not positive semi-definite, scores found from Structure loadingsround(pc1$values,3) [1] 7.998 5.244 4.437 3.932 3.230 2.895 2.761 2.327 2.163 1.994 1.855 1.764 1.613 1.578 1.414 1.366 1.338 1.214 1.156 1.099
[21] 1.042 1.006 0.983 0.961 0.944 0.903 0.886 0.845 0.818 0.803 0.794 0.777 0.768 0.728 0.724 0.691 0.671 0.652 0.645 0.619
[41] 0.605 0.592 0.582 0.574 0.568 0.545 0.525 0.519 0.510 0.504 0.486 0.481 0.475 0.465 0.445 0.444 0.439 0.424 0.409 0.398
[61] 0.390 0.376 0.368 0.342 0.315 0.309 0.233 0.189 0.179 0.149 0.140 0.100 0.091 0.076 0.048 0.029 0.019 0.009 0.006 0.003
[81] 0.000plot(pc1$values, type = "b", main = "Scree plot for Irland dataset",
xlab = "Number of PC", ylab = "Eigenvalue")
# Entre 17 e 22Experiência com 17
pc17 <- principal(data_scaled, nfactors = 17, rotate = "none")Warning: Matrix was not positive definite, smoothing was doneWarning: The matrix is not positive semi-definite, scores found from Structure loadingspc17$loadings
Loadings:
PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12 PC13 PC14 PC15 PC16
PV1SCIE 0.518 -0.413 -0.458 0.184 0.171 -0.111 -0.102 -0.135
PV1READ 0.540 -0.398 -0.489 0.192 0.157 -0.141 0.112
PV1MATH 0.531 -0.360 -0.389 0.150 0.152 -0.156 -0.160 0.102
SOCONPA 0.322 0.322 -0.142 -0.283 -0.174 0.110 0.120 0.110
BODYIMA 0.210 0.374 -0.232 -0.434 0.215 0.150 -0.133
JOYREADP 0.324 -0.238 -0.103 0.123 -0.136 0.203 0.120 -0.103 -0.221
PRESUPP 0.138 -0.139 0.195 -0.188 0.241 0.211 0.195 -0.211 0.246
PASCHPOL 0.282 0.129 0.241 -0.349 0.535 0.142 0.184 0.268 -0.168
PQSCHOOL 0.140 0.236 -0.106 0.162 0.246 -0.348 0.528 0.136 0.204 0.269 -0.174
EMOSUPP 0.169 -0.177 -0.180 0.250 -0.203 0.189 0.212 0.193 0.205
CURSUPP 0.248 -0.109 0.151 0.422 -0.124 0.219 0.152 0.170 -0.226 0.160
INFOJOB2 0.200 0.150 -0.106 0.114 0.154 -0.119 -0.243
INFOJOB1 0.219 0.607 0.138
INFOCAR 0.137 0.128 0.620 0.132 0.109 0.131 0.104 -0.126
ICTOUTSIDE 0.166 0.135 0.214 0.205 0.138 0.187 -0.426 -0.158
ICTCLASS 0.151 0.179 0.172 0.114 0.133 0.146 -0.466 -0.202
SOIAICT 0.190 0.302 0.258 0.426 -0.127 -0.250 0.112 -0.123
AUTICT 0.313 0.568 -0.105 -0.336 -0.103 0.116 0.156
COMPICT 0.291 0.137 0.515 -0.154 -0.342 0.136 0.147 0.226
INTICT 0.221 0.102 0.494 -0.144 -0.176 -0.129 0.167 0.128 0.129 0.307
USESCH 0.272 0.382 0.354 0.153 0.102 0.130 -0.102 -0.347 -0.170 -0.120
HOMESCH 0.184 0.297 0.179 0.393 0.193 -0.109 0.155 0.209 -0.331 -0.137 -0.129
ENTUSE 0.178 0.237 0.128 0.279 0.404 -0.146 -0.143 -0.115 0.114
BEINGBULLIED -0.103 -0.197 0.201 0.417 0.125 -0.107 0.130 -0.228 0.106
BELONG 0.248 0.390 -0.291 -0.338 0.111
MASTGOAL 0.471 0.262 -0.102 0.321 0.110 -0.303
RESILIENCE 0.397 0.424 -0.372 0.110 -0.106
SWBP 0.228 0.451 -0.364 -0.227
EUDMO 0.166 0.513 -0.279 -0.211 0.122 0.126 -0.116
GFOFAIL -0.231 0.346 0.354 -0.151 0.169 -0.326 0.134 0.104 0.157
WORKMAST 0.413 0.296 -0.109 0.277 -0.255 0.116
COMPETE 0.257 0.125 0.166 -0.304 -0.167 -0.172
ATTLNACT 0.308 0.144 0.200 -0.262 0.147 0.116
PERCOOP 0.312 0.401 -0.114 -0.127 0.105 0.180
PERCOMP 0.204 0.116 0.125 0.248 -0.112 -0.249 0.135 0.184 -0.105
PISADIFF -0.471 0.130 0.374 -0.225 -0.154 0.183 0.156 0.122
SCREADDIFF -0.401 0.374 -0.189 -0.130 0.206 -0.112 0.212 0.217 0.167
SCREADCOMP 0.518 -0.333 0.273 0.174 -0.122 0.151 -0.157 -0.202 -0.142
JOYREAD 0.409 -0.170 -0.286 0.137 0.166 0.208 0.334 0.115 -0.142 -0.121 -0.110
TEACHINT 0.331 0.386 -0.265 0.408 0.131 -0.208
ADAPTIVITY 0.310 0.425 -0.207 0.350 0.160 -0.292
STIMREAD 0.358 0.397 -0.200 0.154 0.401 0.154 -0.222
EMOSUPS 0.400 0.301 -0.140 -0.216 -0.157 0.197 -0.161 0.136 0.121 0.154 0.140
PERFEED 0.244 0.370 -0.182 -0.110 0.141 0.308 0.143 -0.266 -0.117
DIRINS 0.195 0.505 0.112 0.377 0.197 -0.265 0.102 -0.117
TEACHSUP 0.268 0.450 -0.130 -0.197 0.405 0.156 -0.309
DISCLIMA 0.215 0.178 -0.241 -0.110 0.109 0.216 0.111
ICTRES 0.485 0.397 -0.157 -0.379 -0.213 0.184 0.176 -0.177
WEALTH 0.447 0.419 -0.186 -0.416 -0.297 0.143 0.123 -0.126
HEDRES 0.541 0.234 -0.252 0.121 -0.113
CULTPOSS 0.550 -0.114 0.141 0.179 -0.178 -0.107 -0.132
HOMEPOS 0.708 -0.134 0.350 -0.119 0.106 -0.387 -0.143 0.160 0.135 -0.166 0.101
ICTSCH 0.259 0.227 0.123 -0.323
ICTHOME 0.386 0.343 -0.311 -0.241 0.109 -0.105
ESCS 0.760 -0.411 0.343 -0.219 0.149
STUBMI -0.109 0.169 0.152 0.193 0.134 -0.159
CHANGE 0.300 0.143 0.387 0.320 0.428 0.247 -0.259 0.178
SCCHANGE 0.211 0.166 0.401 0.327 0.447 0.273 -0.283 0.163 0.124
PC17
PV1SCIE
PV1READ
PV1MATH
SOCONPA -0.169
BODYIMA
JOYREADP
PRESUPP -0.216
PASCHPOL 0.284
PQSCHOOL 0.298
EMOSUPP
CURSUPP -0.164
INFOJOB2
INFOJOB1 0.100
INFOCAR
ICTOUTSIDE -0.148
ICTCLASS -0.120
SOIAICT -0.120
AUTICT -0.179
COMPICT -0.233
INTICT -0.179
USESCH
HOMESCH -0.113
ENTUSE -0.162
BEINGBULLIED 0.250
BELONG -0.126
MASTGOAL 0.166
RESILIENCE 0.120
SWBP -0.119
EUDMO
GFOFAIL
WORKMAST 0.300
COMPETE 0.405
ATTLNACT 0.187
PERCOOP
PERCOMP 0.230
PISADIFF
SCREADDIFF
SCREADCOMP
JOYREAD
TEACHINT
ADAPTIVITY
STIMREAD
EMOSUPS
PERFEED
DIRINS
TEACHSUP
DISCLIMA -0.154
ICTRES
WEALTH
HEDRES
CULTPOSS
HOMEPOS
ICTSCH 0.171
ICTHOME
ESCS
STUBMI
CHANGE
SCCHANGE
[ reached getOption("max.print") -- omitted 23 rows ]
PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12 PC13 PC14 PC15 PC16 PC17
SS loadings 7.998 5.244 4.437 3.932 3.230 2.895 2.761 2.327 2.163 1.994 1.855 1.764 1.613 1.578 1.414 1.366 1.338
Proportion Var 0.099 0.065 0.055 0.049 0.040 0.036 0.034 0.029 0.027 0.025 0.023 0.022 0.020 0.019 0.017 0.017 0.017
Cumulative Var 0.099 0.163 0.218 0.267 0.307 0.342 0.377 0.405 0.432 0.457 0.479 0.501 0.521 0.541 0.558 0.575 0.591pc17r <- principal(data_scaled, nfactors= 17, rotate="varimax")Warning: Matrix was not positive definite, smoothing was doneWarning: The matrix is not positive semi-definite, scores found from Structure loadingspc17r$loadings
Loadings:
RC1 RC4 RC3 RC2 RC7 RC8 RC5 RC13 RC10 RC17 RC6 RC14 RC12 RC15 RC9 RC16
PV1SCIE 0.246 0.787 -0.115 -0.129 -0.113
PV1READ 0.237 0.797 -0.169 0.200 -0.106
PV1MATH 0.257 0.713 0.109 -0.104 -0.164 0.121 -0.108 -0.124
SOCONPA 0.518 0.109 0.120 0.261 0.213
BODYIMA 0.593 -0.414
JOYREADP 0.267 0.236 0.428
PRESUPP 0.632
PASCHPOL 0.167
PQSCHOOL 0.113 0.162
EMOSUPP 0.119 0.103 0.551
CURSUPP 0.104 0.106 0.660
INFOJOB2 0.123 0.163 0.202 -0.218 0.186
INFOJOB1 -0.125 0.624 0.145
INFOCAR 0.611 0.279 0.173
ICTOUTSIDE 0.114 0.617
ICTCLASS 0.619
SOIAICT 0.629 0.193 -0.227
AUTICT 0.224 0.766 -0.103
COMPICT 0.128 0.799
INTICT 0.720 0.113 0.187
USESCH -0.144 0.256 0.102 0.689
HOMESCH -0.130 0.170 0.706 0.118
ENTUSE 0.546 0.315 -0.120
BEINGBULLIED -0.522 0.114 0.351 -0.121
BELONG 0.658
MASTGOAL 0.210 0.283 0.160 0.159 0.501 0.334
RESILIENCE 0.149 0.561 0.144 0.387 -0.130
SWBP 0.663
EUDMO -0.163 0.591 0.261
GFOFAIL -0.493 0.282 0.438
WORKMAST 0.201 0.279 0.120 0.613 0.137
COMPETE 0.108 0.104 0.159 0.570 -0.229 -0.103
ATTLNACT 0.120 0.173 0.375 0.325
PERCOOP 0.413 0.226 0.149 0.141 0.224
PERCOMP 0.229 0.505
PISADIFF -0.713
SCREADDIFF -0.690 -0.109 0.168
SCREADCOMP 0.737 0.109 0.161 0.113
JOYREAD 0.588 0.108 -0.144 0.130 0.131 0.111 0.246 0.213
TEACHINT 0.115 0.102 0.738 0.156
ADAPTIVITY 0.120 0.743
STIMREAD 0.751
EMOSUPS 0.463 0.129 0.195 0.343 0.257
PERFEED 0.659
DIRINS 0.743 -0.114
TEACHSUP 0.113 0.775
DISCLIMA 0.163 0.196 0.366 -0.165 0.189
ICTRES 0.143 0.837 0.105
WEALTH 0.131 0.846
HEDRES 0.158 0.123 0.142 0.120 0.595 0.131 0.101
CULTPOSS 0.241 0.256 0.104 0.467 0.122 0.120 0.231
HOMEPOS 0.275 0.181 0.886 0.117
ICTSCH -0.128 0.113 0.160 0.423 -0.123
ICTHOME 0.655
ESCS 0.846 0.149 0.454
STUBMI -0.128 0.142 0.126 0.276 -0.106
CHANGE -0.111 0.846
SCCHANGE 0.869
RC11
PV1SCIE
PV1READ
PV1MATH 0.125
SOCONPA
BODYIMA
JOYREADP
PRESUPP
PASCHPOL 0.868
PQSCHOOL 0.882
EMOSUPP 0.174
CURSUPP
INFOJOB2 -0.155
INFOJOB1 0.118
INFOCAR
ICTOUTSIDE
ICTCLASS
SOIAICT
AUTICT
COMPICT
INTICT
USESCH
HOMESCH
ENTUSE
BEINGBULLIED
BELONG
MASTGOAL
RESILIENCE
SWBP
EUDMO
GFOFAIL
WORKMAST
COMPETE
ATTLNACT
PERCOOP 0.131
PERCOMP
PISADIFF
SCREADDIFF
SCREADCOMP
JOYREAD
TEACHINT
ADAPTIVITY
STIMREAD
EMOSUPS
PERFEED
DIRINS
TEACHSUP
DISCLIMA
ICTRES
WEALTH
HEDRES
CULTPOSS
HOMEPOS
ICTSCH 0.170
ICTHOME
ESCS
STUBMI
CHANGE
SCCHANGE
[ reached getOption("max.print") -- omitted 23 rows ]
RC1 RC4 RC3 RC2 RC7 RC8 RC5 RC13 RC10 RC17 RC6 RC14 RC12 RC15 RC9 RC16 RC11
SS loadings 4.624 4.369 4.126 4.040 3.703 3.655 2.824 2.391 2.177 2.175 2.134 2.066 2.056 2.014 1.912 1.856 1.789
Proportion Var 0.057 0.054 0.051 0.050 0.046 0.045 0.035 0.030 0.027 0.027 0.026 0.026 0.025 0.025 0.024 0.023 0.022
Cumulative Var 0.057 0.111 0.162 0.212 0.258 0.303 0.338 0.367 0.394 0.421 0.447 0.473 0.498 0.523 0.546 0.569 0.591round(pc17$communality,2) PV1SCIE PV1READ PV1MATH SOCONPA BODYIMA JOYREADP PRESUPP PASCHPOL PQSCHOOL
0.78 0.81 0.70 0.43 0.54 0.35 0.42 0.81 0.83
EMOSUPP CURSUPP INFOJOB2 INFOJOB1 INFOCAR ICTOUTSIDE ICTCLASS SOIAICT AUTICT
0.38 0.51 0.21 0.46 0.51 0.42 0.42 0.53 0.66
COMPICT INTICT USESCH HOMESCH ENTUSE BEINGBULLIED BELONG MASTGOAL RESILIENCE
0.68 0.58 0.60 0.60 0.45 0.46 0.47 0.56 0.54
SWBP EUDMO GFOFAIL WORKMAST COMPETE ATTLNACT PERCOOP PERCOMP PISADIFF
0.47 0.48 0.53 0.54 0.46 0.32 0.35 0.34 0.55
SCREADDIFF SCREADCOMP JOYREAD TEACHINT ADAPTIVITY STIMREAD EMOSUPS PERFEED DIRINS
0.56 0.63 0.56 0.60 0.58 0.61 0.49 0.47 0.60
TEACHSUP DISCLIMA ICTRES WEALTH HEDRES CULTPOSS HOMEPOS ICTSCH ICTHOME
0.63 0.28 0.74 0.76 0.47 0.46 0.93 0.31 0.48
ESCS STUBMI CHANGE SCCHANGE TMINS SMINS LMINS MMINS AGE
0.96 0.18 0.74 0.77 0.97 0.87 0.90 0.77 0.48
GRADE ST061Q01NA ST060Q01NA ST059Q03TA ST059Q02TA ST059Q01TA ST016Q01NA IMMIG HISEI
0.90 0.98 0.95 0.89 0.60 0.90 0.58 0.38 0.72
BFMJ2 BMMJ1 HISCED FISCED MISCED ISCEDL ST001D01T REPEAT ST004D01T
0.50 0.56 0.69 0.51 0.59 0.91 0.90 0.17 0.62 pc17sc <- principal(data_scaled, nfactors=17, rotate="none", scores = TRUE)Warning: Matrix was not positive definite, smoothing was doneWarning: The matrix is not positive semi-definite, scores found from Structure loadingsround(pc17sc$scores,3) PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12 PC13 PC14 PC15
[1,] 5.863 4.928 -1.188 6.371 -0.755 -0.956 3.960 2.413 -0.333 1.082 1.503 -3.071 1.411 0.460 0.576
[2,] 12.039 -3.529 7.401 -0.768 3.638 -0.311 1.536 -0.973 0.811 -1.015 -1.938 1.295 -0.052 1.159 1.132
[3,] 10.790 -1.871 4.974 -3.680 -2.769 2.352 5.409 1.959 0.376 -0.422 -0.515 1.198 0.592 -0.213 -2.138
[4,] 9.288 -1.959 4.737 -1.138 1.392 2.096 -5.097 1.790 -3.715 -2.401 0.981 1.983 -0.288 2.028 0.850
[5,] 9.910 -3.535 -2.194 2.255 -0.255 -1.237 -1.094 -3.491 -3.805 -0.555 1.709 -0.574 -2.504 0.801 1.954
[6,] 3.607 -7.458 5.398 3.713 -2.446 -2.191 1.352 0.204 -4.795 -1.310 2.153 1.058 0.739 2.359 1.013
[7,] 6.314 -8.032 -5.407 4.468 -2.333 -0.160 2.665 -1.827 -3.341 -0.348 1.247 0.825 2.033 -0.137 0.416
[8,] 3.475 -3.276 -4.622 3.459 1.201 -0.427 -3.801 5.605 1.119 1.725 -0.066 0.624 0.287 0.558 0.319
[9,] 1.128 -6.711 -2.254 1.808 -1.276 -4.079 0.727 0.587 -3.236 -1.554 0.755 -1.225 1.452 1.103 -1.418
[10,] 4.810 -2.639 -4.104 -1.048 3.314 -0.198 -0.502 -2.032 1.485 1.888 1.687 -1.628 -1.221 -0.692 -0.496
[11,] 1.973 -3.027 -3.566 0.152 0.330 1.315 3.324 3.473 1.277 3.091 0.108 -1.599 -0.588 -1.347 -3.144
[12,] 7.600 3.863 -2.811 10.505 1.836 -2.166 0.520 1.392 0.031 -1.359 -3.059 2.934 -1.061 0.779 -0.387
[13,] 2.987 3.588 6.830 3.433 -5.310 -3.100 0.651 -0.526 -1.617 0.923 -1.627 0.531 -0.086 1.061 0.759
[14,] 5.375 -3.072 -4.529 3.620 -3.963 -0.860 -1.060 -3.868 -3.197 1.594 1.236 -0.911 -0.306 1.139 -0.106
[15,] 7.527 -6.541 7.476 -4.329 -3.203 0.935 2.651 -0.293 0.811 -2.060 1.409 0.644 0.062 -0.640 -0.761
[16,] 5.938 -1.179 3.425 -2.119 -2.126 3.579 2.449 3.225 1.629 1.013 0.626 -1.379 -0.648 1.915 -1.269
[17,] 22.051 -0.266 -2.689 3.192 3.126 -0.430 -0.888 -0.218 -1.811 -0.727 1.300 0.058 0.788 1.303 0.538
[18,] 6.995 -3.335 -4.982 -0.121 -0.220 1.896 4.388 2.168 0.206 3.381 0.280 -1.558 -0.825 1.354 1.085
[19,] 4.880 -1.945 6.230 2.809 0.274 -1.642 4.949 -1.461 -2.328 3.051 1.423 0.377 0.092 1.309 1.083
[20,] -6.131 5.225 -2.999 -2.370 -5.347 -1.144 0.249 0.518 -1.218 -0.571 -0.324 0.175 0.238 -1.219 1.690
[21,] -1.331 -7.312 -5.563 -3.392 -4.254 1.415 2.838 -0.202 0.452 0.501 -0.027 0.510 -0.059 0.000 0.110
[22,] 3.237 0.001 -4.840 1.671 -4.327 -0.695 -2.225 1.249 -4.330 -0.345 0.568 -0.147 -0.904 -0.038 -0.343
[23,] 7.169 -2.455 -2.876 2.577 -3.430 -2.711 2.428 1.198 0.946 0.825 0.239 -0.965 1.108 1.664 1.227
[24,] 15.583 0.541 5.226 -3.447 -4.414 1.061 1.091 0.921 0.691 -0.764 -1.542 0.396 -1.342 0.884 0.846
[25,] 6.519 -6.200 4.749 -1.344 -0.469 1.963 -0.483 -0.684 -1.533 0.914 0.071 -0.470 0.579 1.222 -1.652
[26,] 6.645 -5.222 -3.734 0.387 -1.587 0.931 3.819 1.295 0.427 0.299 0.446 1.267 -2.454 1.195 0.783
[27,] -0.761 -0.281 3.968 3.822 -1.456 -0.491 -1.193 2.491 -0.923 0.683 0.527 2.114 0.761 -0.500 0.578
[28,] 2.756 -7.455 -3.221 -2.650 -4.715 -1.013 -0.352 1.740 0.281 -1.638 1.762 -0.492 -0.138 0.822 0.860
[29,] 2.988 -6.191 -3.372 0.344 -0.951 -0.591 -1.688 0.834 2.575 -2.150 0.546 1.495 -1.497 -0.575 0.289
[30,] 5.866 -9.897 -3.383 0.292 0.225 0.042 4.036 -0.581 0.129 0.076 1.839 0.198 -1.750 -0.636 0.875
[31,] -2.166 -3.466 5.699 1.429 2.242 -0.076 0.615 5.002 -0.301 3.885 1.598 1.098 -1.964 -0.939 1.442
[32,] 17.875 -3.493 4.690 -6.681 -2.515 2.867 2.292 1.900 2.969 0.578 0.168 -0.734 2.042 0.587 -0.248
[33,] 2.263 5.490 -2.769 -1.101 -7.420 -2.889 0.038 -2.678 0.446 -0.312 1.249 0.857 2.290 -0.536 1.050
[34,] 2.736 -3.447 -4.491 -0.695 1.533 1.270 1.625 -0.770 1.264 0.944 -1.725 -0.926 0.381 1.254 -1.312
[35,] 0.561 -4.032 -4.649 1.828 -3.021 -0.750 3.331 1.393 -3.048 -0.448 0.259 0.905 -1.373 -0.510 -0.375
[36,] 8.826 0.216 5.683 0.921 -2.126 1.934 1.123 3.978 -0.751 2.118 -2.198 -1.229 -0.525 1.167 -1.591
[37,] 1.013 2.809 -1.599 4.709 -1.135 -0.993 2.956 3.188 1.239 -0.563 1.984 2.524 0.779 1.460 2.833
[38,] -7.168 3.516 9.516 5.797 -0.504 4.169 1.304 -0.077 0.595 0.801 0.727 0.629 -1.491 1.516 -3.221
[39,] -1.693 -5.325 -2.999 -0.240 8.450 -1.868 -4.855 2.746 -6.321 -2.586 0.487 0.278 1.500 -1.665 -1.793
[40,] -7.649 -4.121 0.553 7.796 3.643 -4.740 -3.862 0.676 2.182 -0.204 0.682 1.549 3.030 0.318 1.968
[41,] -3.731 -6.393 -3.818 8.774 -2.390 -2.096 5.905 0.222 -1.143 -1.239 0.342 -0.967 -0.463 0.329 0.355
[42,] -13.626 18.159 -6.165 -12.601 -2.392 1.231 -1.587 -1.803 1.190 1.131 0.588 -1.284 -0.791 -0.567 0.765
[43,] -19.116 -10.217 -2.711 -0.126 1.921 -1.690 -2.373 0.620 -2.352 -0.270 -0.051 1.530 -1.731 0.371 0.666
[44,] -1.481 -1.843 -2.101 3.136 -2.228 -3.365 1.549 0.732 0.325 1.351 -0.766 0.640 0.128 -0.329 0.614
[45,] -1.055 2.958 -2.572 3.721 7.153 3.369 -0.405 0.000 1.931 -0.142 3.862 4.542 -1.337 0.578 -0.130
[46,] -10.336 1.070 -2.766 0.899 -0.587 -1.496 4.578 -2.201 -0.070 0.169 -0.532 0.983 -1.568 0.838 -0.317
[47,] -10.637 8.149 -3.571 -0.596 3.274 -0.897 0.814 -0.757 1.120 2.201 2.568 -1.964 0.562 -0.393 -0.262
[48,] 2.383 -5.153 6.656 -0.728 0.654 2.798 -0.362 0.433 -2.666 1.003 0.720 -0.279 -1.649 0.785 0.599
[49,] -4.953 -2.940 4.864 -2.722 5.050 2.523 1.716 1.590 -1.292 -1.488 2.300 3.476 -0.857 -0.060 1.248
[50,] -6.196 5.469 -7.117 -5.445 0.287 4.215 2.749 2.819 1.328 -0.570 -2.202 -0.059 0.301 0.256 0.665
[51,] -14.213 -1.523 -0.861 1.651 5.316 -4.914 -1.039 -5.667 0.625 2.936 0.308 -0.211 -1.708 -1.759 1.298
[52,] -10.490 -1.218 -1.972 -1.428 3.727 -1.665 -4.399 0.165 -1.893 -0.199 -1.007 -0.922 -0.271 1.001 0.267
[53,] -4.859 8.510 -4.774 -4.417 -4.769 0.508 -0.411 0.711 -0.018 -1.729 3.107 1.090 -0.035 0.127 -0.440
[54,] -6.933 4.981 -0.376 5.618 0.349 -3.291 -1.912 -0.622 2.372 -1.176 0.264 2.075 -1.130 -0.819 -0.398
[55,] -15.415 4.832 8.217 0.795 2.885 -0.420 3.896 1.792 0.780 -1.155 1.387 2.228 2.364 -0.441 0.634
[56,] -20.573 -0.484 -4.508 5.698 7.522 -2.668 -3.194 -0.026 0.346 4.470 4.785 -4.677 -0.546 -0.492 -0.326
[57,] -4.846 9.392 -3.959 -6.505 1.535 0.352 -0.127 1.185 -0.790 -3.069 1.256 1.869 -2.937 0.899 1.839
[58,] -3.451 7.177 -2.894 -5.775 2.667 -0.473 1.405 0.303 -1.524 -0.629 0.652 0.994 0.001 -0.326 1.212
PC16 PC17
[1,] -2.811 -0.502
[2,] -0.234 0.185
[3,] 0.390 0.083
[4,] 0.331 -0.632
[5,] -0.610 0.984
[6,] 0.440 0.830
[7,] -0.227 -0.683
[8,] 0.623 0.225
[9,] -0.095 0.403
[10,] 0.301 -0.378
[11,] -0.377 1.159
[12,] -0.479 1.647
[13,] 0.440 0.459
[14,] -0.266 -0.033
[15,] 0.438 -0.934
[16,] -0.253 -0.043
[17,] -1.969 2.186
[18,] -0.655 -0.197
[19,] 2.100 -1.196
[20,] -1.446 -1.219
[21,] 0.087 -1.078
[22,] -1.022 0.555
[23,] 0.856 0.251
[24,] -0.726 1.415
[25,] -0.043 3.657
[26,] 0.398 0.356
[27,] 0.062 0.836
[28,] -0.356 1.568
[29,] -0.738 0.668
[30,] -0.963 -0.011
[31,] 1.321 0.970
[32,] -0.013 0.760
[33,] 0.318 -1.875
[34,] -0.525 0.474
[35,] -0.402 -0.035
[36,] -0.277 0.319
[37,] 0.098 0.971
[38,] -1.767 1.454
[39,] 0.287 0.564
[40,] 0.652 -2.595
[41,] -2.775 -0.643
[42,] -0.343 -2.363
[43,] -0.587 1.009
[44,] 0.496 0.226
[45,] -0.885 -0.405
[46,] -0.666 0.950
[47,] 0.775 -1.174
[48,] -1.040 1.384
[49,] -1.044 -1.090
[50,] 0.935 -0.143
[51,] 0.006 0.152
[52,] -0.749 0.854
[53,] -0.597 0.716
[54,] -0.955 -0.009
[55,] -1.722 2.306
[56,] 0.435 -0.999
[57,] -1.319 0.780
[58,] -0.999 1.588
[ reached getOption("max.print") -- omitted 5519 rows ]mean(pc17sc$scores[,1])[1] 0.0000000000000003497933sd(pc17sc$scores[,1])[1] 7.99831Experiência com 12
pc12$loadings
Loadings:
PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12
PV1SCIE 0.520 -0.416 -0.456 0.167 0.168 -0.126 -0.108
PV1READ 0.541 -0.400 -0.489 0.176 0.159
PV1MATH 0.533 -0.363 -0.387 0.135 0.138 -0.169
SOCONPA 0.322 0.323 -0.141 -0.281 -0.178 0.111
BODYIMA 0.210 0.373 -0.227 -0.436 0.220 0.152
JOYREADP 0.323 -0.236 -0.106 0.121 0.172 0.185
PRESUPP 0.137 -0.147 0.189 0.204 0.227 0.220
PASCHPOL 0.282 0.134 0.224 0.375 0.525 0.161
PQSCHOOL 0.141 0.237 0.164 0.228 0.375 0.517 0.151
EMOSUPP 0.169 -0.179 -0.190 0.239 0.223 0.172 0.223
CURSUPP 0.246 -0.109 0.149 0.418 0.162 0.205 0.139
INFOJOB2 0.198 0.156 -0.105 0.119
INFOJOB1 0.225 0.598 0.162
INFOCAR 0.140 0.133 0.613 0.149 0.107 0.147 0.109
ICTOUTSIDE 0.166 0.135 0.216 0.212 0.143 0.186
ICTCLASS 0.151 0.173 0.177 0.118 0.138 0.143 -0.117
SOIAICT 0.190 0.300 0.249 0.437 -0.145 -0.232 0.108 0.125 0.106
AUTICT 0.314 0.575 -0.125 -0.317 -0.104
COMPICT 0.292 0.135 0.525 -0.174 -0.318 0.156
INTICT 0.222 0.501 -0.152 -0.153 -0.135 0.182
USESCH 0.275 0.369 0.361 0.162 0.110 0.126 0.109
HOMESCH 0.184 0.299 0.167 0.392 0.204 0.166 0.207
ENTUSE 0.178 0.238 0.121 0.270 0.415 -0.158 -0.120 0.121 0.106
BEINGBULLIED -0.103 -0.198 0.197 0.419 0.134 -0.118
BELONG 0.248 0.389 -0.288 -0.334
MASTGOAL 0.470 0.263 0.321 -0.307
RESILIENCE 0.397 0.423 -0.374 0.112 -0.105
SWBP 0.228 0.452 -0.361 -0.225
EUDMO 0.165 0.514 -0.270 -0.210 0.131 -0.115 -0.117
GFOFAIL -0.231 0.342 0.358 -0.155 0.167 -0.334 0.105
WORKMAST 0.414 0.296 -0.105 0.276 -0.256
COMPETE 0.258 0.122 0.169 -0.298 -0.172
ATTLNACT 0.308 0.144 0.200 -0.261
PERCOOP 0.313 0.400 -0.114
PERCOMP 0.204 0.116 0.123 0.251 0.123 -0.257 0.112
PISADIFF -0.472 0.134 0.382 -0.212 -0.127 0.195 -0.102 0.165
SCREADDIFF -0.402 0.381 -0.179 -0.103 0.216 -0.122 0.217
SCREADCOMP 0.518 -0.340 0.263 0.161 -0.139 0.147 -0.175
JOYREAD 0.410 -0.172 -0.286 0.124 0.198 0.187 0.335 -0.164
TEACHINT 0.331 0.385 -0.267 0.142 0.394 0.135 -0.204
ADAPTIVITY 0.311 0.423 -0.209 0.103 0.341 0.167 -0.289
STIMREAD 0.358 0.396 -0.199 0.147 0.137 0.388 0.158 -0.218
EMOSUPS 0.398 0.303 -0.137 -0.214 -0.164 0.198 -0.171 0.141
PERFEED 0.243 0.369 -0.187 -0.111 0.138 0.302 0.145 -0.263
DIRINS 0.195 0.504 0.108 0.370 0.203 -0.259
TEACHSUP 0.268 0.448 -0.131 -0.198 0.397 0.164 -0.307
DISCLIMA 0.214 0.177 -0.241 -0.117 0.131 0.199 -0.112
ICTRES 0.485 0.397 -0.155 -0.376 -0.207 -0.221 0.193 -0.128
WEALTH 0.447 0.418 -0.192 -0.412 -0.294 -0.185 0.138
HEDRES 0.540 0.241 0.100 -0.255 -0.133
CULTPOSS 0.548 -0.112 0.145 0.175 -0.181 -0.100 -0.103
HOMEPOS 0.707 -0.131 0.353 -0.103 0.115 -0.386 -0.139 -0.188 0.150 -0.134
ICTSCH 0.260 0.231 0.128
ICTHOME 0.386 0.345 -0.306 -0.243 0.120
ESCS 0.760 -0.409 0.350 -0.215 0.149
STUBMI -0.110 0.176 0.164 0.172
CHANGE 0.292 0.149 0.390 0.349 -0.423 0.268 -0.204
SCCHANGE 0.201 0.169 0.404 0.357 -0.443 0.296 -0.223
TMINS 0.142 -0.118 0.379 0.479 0.429 -0.112 -0.431
SMINS -0.299 0.202 0.591 -0.138
LMINS -0.125 -0.265 0.289 0.554 -0.114 -0.103 0.221
MMINS -0.360 0.363 0.621 -0.100 0.113
AGE 0.652 0.190
GRADE 0.837 0.375 0.120
ST061Q01NA 0.315 0.446 0.359 -0.113 -0.365
ST060Q01NA 0.111 -0.103 0.248 0.284 0.283 -0.278
ST059Q03TA -0.304 0.390 -0.146 -0.235 0.140 0.266
ST059Q02TA -0.437 0.123 0.317 -0.103 -0.115 -0.233 0.341
ST059Q01TA -0.289 0.127 0.344 -0.119 -0.333 0.463
ST016Q01NA 0.255 0.463 -0.439 -0.303
IMMIG 0.106 0.187 0.302 0.171 -0.314 0.139 -0.197
HISEI 0.528 -0.418 0.204 -0.239 0.319 0.132 0.138
BFMJ2 0.481 -0.354 0.159 -0.156 0.219
BMMJ1 0.461 -0.371 0.163 -0.219 0.280 0.143 0.135
HISCED 0.492 -0.372 0.258 -0.182 0.411 -0.104 0.145
FISCED 0.473 -0.325 0.199 -0.145 0.325
MISCED 0.458 -0.337 0.228 -0.178 0.375 -0.101 0.130
ISCEDL 0.904 0.266
ST001D01T 0.837 0.375 0.120
REPEAT -0.109 -0.176 0.104 0.136 0.191
ST004D01T 0.107 0.117 0.107 -0.378 0.274 -0.288 0.196 0.304
PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12
SS loadings 8.006 5.241 4.442 3.959 3.230 2.882 2.761 2.331 2.163 1.992 1.856 1.736
Proportion Var 0.099 0.065 0.055 0.049 0.040 0.036 0.034 0.029 0.027 0.025 0.023 0.021
Cumulative Var 0.099 0.164 0.218 0.267 0.307 0.343 0.377 0.406 0.432 0.457 0.480 0.501pc12sc <- principal(data_scaled, nfactors=12, rotate="none", scores = TRUE)
round(pc12sc$scores,3)
mean(pc12sc$scores[,1])
sd(pc12sc$scores[,1])---
title: "Projeto"
author: "grupo 6sigma"
date: "2023-05-10"
output: html_notebook
---

### Bibliotecas
```{r}
library(skimr)
library(txtplot)
library(cluster)
library(tidyverse)
library(caret)
library(factoextra)
library(mclust)
library(readr)
library(naniar)
library(here)
library(psych)
library(corrplot)
library(haven)
library(dplyr)
library(magrittr)
options(repr.plot.width=6, repr.plot.height=6)
```

# Leitura dos dados
```{r}
dados <- readRDS(here("data", "STU_QQQ_5.rds"))
dados
```


## Omissos de cada coluna
```{r}
colSums(is.na(dados))
```

```{r}
# A coluna dos Paises não tem nulos
sum(is.na(dados$CNT))
```

## Países Existentes na base de dados
```{r}
unique(dados$CNT)
```

### Visualização das características de cada um dos países
```{r}
dados %>%
  mutate(NumNulos = rowSums(is.na(.))) %>%
  group_by(CNT) %>%
  summarize(TotalNulos = sum(NumNulos),
            NumLinhas = n(),
            PercentNulos = (TotalNulos / (NumLinhas * ncol(dados))) * 100,
            MeanScience = mean(PV1SCIE, na.rm = TRUE),
            MeanRead = mean(PV1READ, na.rm = TRUE),
            MeanMath = mean(PV1MATH, na.rm = TRUE),
            .groups = "drop") %>% 
  arrange(desc(NumLinhas))
```

# Irlanda
```{r}
Irlanda <- dados %>% filter(CNT == "IRL")
Irlanda
```

## Características da Irlanda
```{r, results='hide'}
dim(Irlanda)
describe(Irlanda)
```

```{r}
# str(Irlanda)
```

```{r}
# summary(Irlanda)
```

```{r}
skimr::skim(Irlanda)
```

```{r}
summary(Irlanda)
```


## Limpeza dos valores omissos

```{r}
IrlandaSemiLimpa <- Irlanda %>% select(where(~ !all(is.na(.))))
dim(IrlandaSemiLimpa)
```
```{r}
nulos_percent <- IrlandaSemiLimpa %>%
  summarise_all(~ mean(is.na(.)) * 100)

colunas_com_nulos <- colnames(nulos_percent)[nulos_percent > 30]
colunas_com_nulos
```
```{r}
Irlanda <- select(Irlanda, -one_of(colunas_com_nulos))
```


```{r}
options(scipen = 999)
Irlanda %>%
  summarise(across(where(is.labelled), \(x) attributes(x)$label)) %>% 
  tidylog::pivot_longer(everything(), names_to = "Column", values_to = "Description") %>% 
  rowwise() %>% 
  mutate(Type = sapply(Column, \(x) class(dados[[x]])[3])) %>% 
  mutate(First_5_Values = sapply(Column, \(x) paste(head(na.omit(dados[[x]]), 5) , collapse = ", "))) %>%
  mutate(naPercent = sapply(Column, \(x) Irlanda %>% zap_labels() %>% .[[x]] %>% is.na() %>% mean %>% "*"(100) %>% round(2))) -> dfI.desc
dfI.desc %>% View()
dfI.desc
```

```{r}
library(mice)
Irlanda <- IrlandaSemiLimpa %>% zap_labels() %>% mice(method = "norm.predict", m = 1)
Irlanda <- complete(Irlanda)

Irlanda %>% summarise_all(~ mean(is.na(.)) * 100)
```

```{r, results='hide'}
describe(Irlanda)
```
```{r}
colunas_nao_numericas <- names(Irlanda)[!sapply(Irlanda, is.numeric)]
colunas_nao_numericas
```
```{r}
valores_unicos <- lapply(Irlanda[colunas_nao_numericas], unique)

# Exibir os valores únicos de cada coluna
for (i in seq_along(valores_unicos)) {
  coluna <- colunas_nao_numericas[i]
  valores <- valores_unicos[[i]]
  cat("Valores únicos da coluna", coluna, ":\n")
  print(valores)
  cat("\n")
}
```


```{r}
colunas_nao_numericas <- colunas_nao_numericas[-5]
colunas_nao_numericas

Irlanda[colunas_nao_numericas] <- lapply(Irlanda[colunas_nao_numericas], as.factor)
Irlanda <- Irlanda %>% select_if(is.numeric)
```

# Tratamento dos dados
  Após realizada a limpeza dos dados, podemos então passar finalmente para a criação de PCA, a fim de se conseguir reduzir a dimensionalidade da base de dados, que possui 86 colunas.
  Antes de prosseguir, é necessário reter alguns pontos sobre como aplicar o PCA:

  1. O PCA é mais eficaz quando as variáveis estão correlacionadas entre si.

  2. Antes de aplicar o PCA, é importante verificar se as variáveis estão em escalas comparáveis. Variáveis em diferentes escalas podem ter diferentes variações e isso pode distorcer a análise do PCA.
  
## Correlações das colunas
```{r, results='hide'}
#Correlation plot with colors (too many attributes)

correlation <- cor(Irlanda)
corrplot(correlation)

#Correlation matrix
round(correlation, 3)
```

## KMO measure
```{r}
KMO(correlation)
```
O KMO (Kaiser-Meyer-Olkin) é uma medida estatística utilizada para avaliar a adequação dos dados para realizar uma análise de fator ou análise de componentes principais (PCA).
O valor do KMO varia de 0 a 1 e, quanto mais próximo de 1, melhor a adequação dos dados para uma análise de fator ou PCA. Valores de KMO acima de 0,6 são geralmente considerados adequados para análises exploratórias, enquanto valores acima de 0,8 são considerados muito bons.

### Possiveis variáveis fracas
Vamos então visualizar as variáveis que não são adequadas para aplica o PCA

```{r}
# kmo_result <- KMO(correlation)
# str(kmo_result)
# Obtém as variáveis com KMO abaixo de 0.6
# low_kmo_variables <- kmo_result$MSAi[kmo_result$MSAi < 0.6]
# length(low_kmo_variables)
# low_kmo_variables
```
### Possiveis variáveis fortes

```{r}
# Obtém as variáveis com KMO acima, ou igual, a 0.8
# high_kmo_variables <- kmo_result$MSAi[kmo_result$MSAi >= 0.8]
# length(high_kmo_variables)
# high_kmo_variables
```

## Teste de esfericidade de Bartlett em uma matriz de correlação.

O teste de esfericidade de Bartlett é um teste estatístico que avalia se as variáveis em um conjunto de dados estão correlacionadas entre si. Ele verifica a hipótese nula de que a matriz de correlação populacional é uma matriz de identidade, o que significa que as variáveis são independentes e não correlacionadas.

```{r}
cortest.bartlett(correlation)
```
Se o valor-p (p-value) calculado no teste de esfericidade de Bartlett for menor que 0.05, isso sugere evidências estatísticas para rejeitar a hipótese nula de que as variáveis são independentes e não correlacionadas, por outras palavras, existe uma correlação significativa entre as variáveis em questão. Isso significa que as variáveis não podem ser consideradas independentes e que existe algum grau de relação entre elas.

# Estandardizar os dados

```{r}
data_scaled <- scale(Irlanda)
data_scaled %>% glimpse()
```

# PCA

Nesta parte, passaremos então para o processo de criação de PCA's. Podemos então começar por pensar algumas ideias por alto, podendo ser estas:

* **PCA de Desempenho Acadêmico**:
    - Variáveis de interesse: Pontuações em diferentes domínios acadêmicos, como leitura, matemática e ciências.
    - Objetivo: Compreender a estrutura subjacente do desempenho acadêmico dos alunos e identificar padrões gerais de habilidades e competências.

* **PCA de Fatores Socioeconômicos**:
    - Variáveis de interesse: Variáveis socioeconômicas, como o nível de educação dos pais, ocupação dos pais, renda familiar, acesso a recursos educacionais, etc.
    - Objetivo: Explorar as relações entre os fatores socioeconômicos e o desempenho acadêmico dos alunos, bem como identificar grupos de alunos com perfis socioeconômicos semelhantes.

* **PCA de Atitudes e Comportamentos**:
    - Variáveis de interesse: Variáveis relacionadas a atitudes, motivação, autoeficácia, disciplina, participação em atividades extracurriculares, etc.
    - Objetivo: Identificar padrões de atitudes e comportamentos dos alunos que podem influenciar seu desempenho académico e bem-estar geral.

* **PCA de Ambiente Escolar**:
    - Variáveis de interesse: Variáveis relacionadas ao ambiente escolar, como tamanho da turma, recursos educacionais disponíveis, clima escolar, relacionamento professor-aluno, etc.
    - Objetivo: Explorar a relação entre o ambiente escolar e o desempenho dos alunos, bem como identificar características importantes do ambiente que afetam o aprendizado.

## Colunas

```{r}
colunas <- colnames(data_scaled)
colunas
```
## Visualizar correlações interessantes
```{r}
correlation1 <- cor(data_scaled[, -1], data_scaled[, 1])
# corrplot(correlation1)
# Definir o limite de correlação desejado
limite_correlacao <- 0.3

# Identificar as colunas com correlação forte
colunas_fortes <- row.names(correlation1)[which(abs(correlation1) > limite_correlacao)]

colunas_fortes <- append(colunas_fortes, "PV1SCIE")
  
# Exibir as colunas com correlação forte
print(colunas_fortes)
```


## Criação do PCA

```{r}
pc1 <- principal(data_scaled, nfactors=ncol(data_scaled), rotate="none")
round(pc1$values,3)
```
```{r}
plot(pc1$values, type = "b", main = "Scree plot for Irland dataset", 
     xlab = "Number of PC", ylab = "Eigenvalue")
# Entre 17 e 22
```
## Experiência com 17

```{r}
pc17 <- principal(data_scaled, nfactors = 17, rotate = "none")
pc17$loadings
pc17r <- principal(data_scaled, nfactors= 17, rotate="varimax")
pc17r$loadings
round(pc17$communality,2)
```

```{r}
pc17sc <- principal(data_scaled, nfactors=17, rotate="none", scores = TRUE)

round(pc17sc$scores,3)
mean(pc17sc$scores[,1])
sd(pc17sc$scores[,1])
```
## Experiência com 12
```{r}
pc12 <- principal(data_scaled, nfactors = 12, rotate = "none")
pc12$loadings
pc12r <- principal(data_scaled, nfactors= 12, rotate="varimax")
pc12r$loadings
round(pc12$communality,2)
```

```{r}
pc12sc <- principal(data_scaled, nfactors=12, rotate="none", scores = TRUE)

round(pc12sc$scores,3)
mean(pc12sc$scores[,1])
sd(pc12sc$scores[,1])
```

